Context-aware intelligent recommendation system for tourism

Meehan, Kevin; Lunney, Tom; Curran, Kevin; McCaughey, Aiden

doi:10.1109/percomw.2013.6529508

Cited by 120 publications

(105 citation statements)

References 9 publications

(9 reference statements)

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“…Personalized recommendation approaches in TRS aim to void information overload and offer only relevant information to the tourist [11], they recommend a list of tourism items matching personal preferences and take note of tourist experiences [23]. This is the aspect where personalized recommendation approaches demonstrate their importance and enhance the quality of recommendations, since they consider each user having unique, special characteristics and its own experiences.…”

Section: Personalized Approachesmentioning

confidence: 99%

“…Recommendation systems are called context-aware when they use the context in its calculation to predict items likely to interest the user [11].…”

Section: Context-aware Approachesmentioning

confidence: 99%

“…These devices are able to capture and to provide many information that could enrich the current context as well as its variations. However, this aspect is the most challenging part in these type of RS; since it demand the definition of which context element is relevant to the recommendation, and which one provide more accuracy [11].…”

Section: Context-aware Approachesmentioning

confidence: 99%

“…On the other side, from the tourists' point of view, RS could be a valuable help while preparing a trip or searching a service among many destinations, numerous attractions and activities. The use of RS could help tourists to save time and energy while searching for a trip and or services that match their preferences and interests [10] [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tourism Recommender Systems: An Overview of Recommendation Approaches

Kzaz¹,

Dakhchoune²,

Dahab³

2018

IJCA

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Recommender systems have become an active research topic during the last two decades, thus giving rise to several approaches and techniques. They have also become increasingly popular among practitioners and used in variety of areas including movies, news, books, research articles restaurants, garments, financial services, insurance, social tags and products in general. Tourism is an important sector for economic development and a potential application area of use of recommender systems. This paper presents an overview of existing recommender approaches used in tourism and discusses their relevance taking into account tourism context and specificities.

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Section: Personalized Approachesmentioning

confidence: 99%

“…Recommendation systems are called context-aware when they use the context in its calculation to predict items likely to interest the user [11].…”

Section: Context-aware Approachesmentioning

confidence: 99%

Section: Context-aware Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tourism Recommender Systems: An Overview of Recommendation Approaches

Kzaz¹,

Dakhchoune²,

Dahab³

2018

IJCA

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“…We have labeled those systems online, as most of them were inspired by common features of social network platforms, providing access to usergenerated content. Their primary design goal is to allow collaboration among users and support them in their visits, for example, using Twitter (https://twitter.com) or Facebook (https://www.facebook.com) as communication media (moreTourism [32] and e-Tourism [33]), obtaining the users' valuation of different POIs from them (Im feeling LoCo [34], iTravel [17], VISIT [18], and PlanTour [19]), or grouping nearby users for a joint activity (e-Tourism [33], iTravel [17], and Swarm [4]). …”

Section: Related Workmentioning

confidence: 99%

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

López-Matencio¹,

Vales‐Alonso²,

Costa-Montenegro³

2017

Mobile Information Systems

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In this work, we propose ANT, a network of agents which exchange information in a stigmergy-based fashion. In ANT, each system's actor distributes pheromones as a way of indicating places' attractiveness, as well as for building a proper routing path to those sites. The goal of ANT is to improve the chances of discovering points of interest, as well as to reduce the time required for doing so. We have applied ANT to a tourist mobility scenario, where both people and things (events, restaurants, performances, etc.) participate. ANT has achieved notable success in this example case. We find that probability of discovering temporary events and dates improves by more than 35%, while the mean time employed to determine static point decreases by more than a third. We also introduce a mobile-based architecture which performs ANT tasks efficiently and easily for the user.

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Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

Krishnan

Mohammed

Alnoor

et al. 2021

Int J of Intelligent Sys

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The benchmarking of smart e‐tourism data management applications falls under the problem of multicriteria decision‐making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e‐tourism data management applications of each category and subcategory in smart e‐tourism. In the second phase, the integration of the IT2TR‐FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e‐tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real‐time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context‐awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e‐tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.

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Context-aware intelligent recommendation system for tourism

Cited by 120 publications

References 9 publications

Tourism Recommender Systems: An Overview of Recommendation Approaches

Tourism Recommender Systems: An Overview of Recommendation Approaches

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

Interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e‐tourism applications

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